White Matter Microstructure in Attention-Deficit/Hyperactivity Disorder
Damatac, Christienne G; Chauvin, Roselyne J M; Zwiers, Marcel P; van Rooij, Daan;
Akkermans, Sophie E A; Naaijen, Jilly; Hoekstra, Pieter J; Hartman, Catharina A; Oosterlaan,
Jaap; Franke, Barbara
Published in:
Biological psychiatry. Cognitive neuroscience and neuroimaging
DOI:
10.1016/j.bpsc.2020.07.015
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Damatac, C. G., Chauvin, R. J. M., Zwiers, M. P., van Rooij, D., Akkermans, S. E. A., Naaijen, J., Hoekstra,
P. J., Hartman, C. A., Oosterlaan, J., Franke, B., Buitelaar, J. K., Beckmann, C. F., & Sprooten, E. (2020).
White Matter Microstructure in Attention-Deficit/Hyperactivity Disorder: A Systematic Tractography Study in
654 Individuals. Biological psychiatry. Cognitive neuroscience and neuroimaging, 1-10.
https://doi.org/10.1016/j.bpsc.2020.07.015
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Archival Report
White Matter Microstructure in Attention-De
ficit/
Hyperactivity Disorder: A Systematic
Tractography Study in 654 Individuals
Christienne G. Damatac, Roselyne J.M. Chauvin, Marcel P. Zwiers, Daan van Rooij,
Sophie E.A. Akkermans, Jilly Naaijen, Pieter J. Hoekstra, Catharina A. Hartman,
Jaap Oosterlaan, Barbara Franke, Jan K. Buitelaar, Christian F. Beckmann, and Emma Sprooten
ABSTRACT
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by age-inappropriate levels of inattention and/or hyperactivity-impulsivity. ADHD has been related to differences in white matter (WM) microstructure. However, much remains unclear regarding the nature of these WM differences and which
clinical aspects of ADHD they reflect. We systematically investigated whether fractional anisotropy (FA) is associated
with current and/or lifetime categorical diagnosis, impairment in daily life, and continuous ADHD symptom measures. METHODS: Diffusion-weighted imaging data were obtained from 654 participants (322 unaffected, 258 affected, 74 subthreshold; 7–29 years of age). We applied automated global probabilistic tractography on 18 major WM pathways. Linear mixed-effects regression models were used to examine associations of clinical measures with overall brain and
tract-specific FA.
RESULTS: There were significant interactions of tract with all ADHD variables on FA. There were no significant as-sociations of FA with current or lifetime diagnosis, nor with impairment. Lower FA in the right cingulum angular bundle
was associated with higher hyperactivity-impulsivity symptom severity (pfamilywise error = .045). There were no
significant effects for other tracts.
CONCLUSIONS: This is the first time global probabilistic tractography has been applied to an ADHD dataset of this
size. We found no evidence for altered FA in association with ADHD diagnosis. Ourfindings indicate that associations
of FA with ADHD are not uniformly distributed across WM tracts. Continuous symptom measures of ADHD may be more sensitive to FA than diagnostic categories. The right cingulum angular bundle in particular may play a role in symptoms of hyperactivity and impulsivity.
Keywords: ADHD, Diffusion MRI, Dimensional, Neuroimaging, Tractography, White matter
https://doi.org/10.1016/j.bpsc.2020.07.015
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate levels of inattention (IA) and/or hyperactivity-impulsivity (HI). Approximately 5% of children worldwide are diagnosed with ADHD, and about 15% of youths with ADHD retain a full diagnosis, while around 70% retain impairing symptoms of the disorder in adulthood (1,2). Using diffusion-weighted imaging (DWI), studies have reported alterations in white matter (WM) microstructural properties across the life-span of people with ADHD, some of which had an overlapping sample with the current study (summarized in Table S12) (3–11). Generally, however, limited sample sizes and method-ological differences between studies contribute to in-consistencies in the locations and directions offindings thus far, including studies within the same sample (Table S12). Previous studies leave several unanswered questions that are critical for understanding the clinical relevance of DWIfindings,
especially whether WM microstructural properties are associ-ated with 1) trait versus state effects in ADHD, 2) continuous symptom measures versus categorical diagnosis of ADHD, and 3) clinical impairment in ADHD. Here, we applied auto-mated tractography to DWI data from a large cohort of par-ticipants with and without ADHD to address such questions.
DWI measures the magnitude and direction of water diffu-sion, which in WM reflects the underlying organization of axons and their surrounding myelin (12–14). With diffusion tensor analysis, different metrics of water diffusion are calculated in each voxel, such as the degree of its directional preference (fractional anisotropy [FA]), which is the most commonly investigated metric and the one we focus on here. Although FA is not a direct measure of physiological cellular properties, it is assumed to reflect a combination of the degree of parallel organization of axons, their packing density, and the amount and integrity of their myelin sheath (15,16). In ADHD
ª 2020 Society of Biological Psychiatry. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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case-control studies to date, lower FA in ADHD has been found in many locations, most frequently in interhemispheric, frontal, and temporal regions; yet, elevated FA has also been reported (3). Thus, many case-control studies show some differences in FA in ADHD, but the nature and anatomical lo-cations of thesefindings are inconsistent.
FA has mostly been related to diagnosis at a single time point, typically at the time of DWI acquisition. However, remission occurs in some patients, as do fluctuations in symptom severity and impairment over time (2). Typical case-control designs cannot disentangle stable lifetime trait effects associated with ADHD from those associated with ADHD as a current state. A better understanding of these dynamics has implications for assumptions behind genetic liability for ADHD, for the nature of the neural mechanisms underlying ADHD, and their potential receptiveness to treatment. Here, we refer to associations based on patients’ current diagnoses as “state” effects, and to associations related to ever-affected individuals regardless of current diagnosis as“trait” effects. Trait effects remain identifiable in remittent patients, pointing to the possi-bility that the diagnosis of ADHD at any point leaves an indelible “mark” on WM that persists throughout life, regard-less of the disorder’s progression (10). In longitudinal studies, decreased FA has been found as a trait effect of ADHD in thalamocortical tracts and the superior longitudinal fasciculus, irrespective of diagnostic outcome. However, in other studies, no such trait effects were found (10,17). Larger sample sizes combined with sophisticated DWI analytical techniques may clarify inconsistencies in these previousfindings.
Unlike the hard line of diagnostic category, ADHD symp-toms are continuously distributed throughout the population, and the boundary between those with and without the disorder is ill-defined (18–20). Considering continuous variables of symptom severity, rather than categorical diagnosis, could increase power to detect ADHD-related cognitive processes and brain traits that are also continuously distributed. Nonclinical“control” participants may also exhibit subclinical ADHD characteristics, and within patient groups, there is considerable variation in symptom severity. Dimensional ana-lyses allow for modeling of the entire spectrum of ADHD, from minor subthreshold symptoms to clinically extreme symptoms (21). Generally, however, DWI studies that have applied dimensional analyses have been inconsistent in terms of anatomical location and have suggested that within the patient population, increased symptom severity ratings are associated with higher FA in widespread brain regions (9,22). Given that the categorical diagnosis of ADHD has generally been asso-ciated with reduced FA, a more in-depth study of these counterintuitive effects is warranted.
The sole presence of (sufficient) symptoms does not constitute a diagnosis of ADHD. For a clinical diagnosis, symptoms must be accompanied by impairment in daily functioning at home, at school or work, and/or in social set-tings. The degree of impairment does not directly map onto diagnosis or symptom severity scores. To understand the nature of case-control differences and their contrast with as-sociations with symptom dimensions, an understanding of how clinical impairment is associated with brain differences is equally necessary. Although impairment may be a more sub-jective measure than symptom criteria, it is arguably the most
impactful factor in the quality of life of patients. Impairment may be predictive of emotional lability in ADHD, independent of symptom severity (23). Magnetic resonance imaging (MRI) studies that considered clinical impairment specifically, inde-pendent of diagnosis or symptom severity, are scarce to date. An additional origin of discrepancies between studies may be found in methodological differences. The use of voxelwise analyses in the presence of crossing fibers and anatomical differences in tract width and shape can lead to ambiguity in the anatomical location of DWIfindings. Additionally, residual effects of head motion after realignment tend to be associated with DWI outcome measures but are not always taken into account (3). Previous tractography methods required user interaction (e.g., manually draw regions of interest or set thresholds for path angle and length), involved a priori selec-tion of tracts, and employed local tractography (24). In an effort to improve large dataset analysis in a data-driven manner, we applied tractography to one of the largest cohorts of in-dividuals with ADHD and healthy controls. We applied global probabilistic tractography in combination with anatomical knowledge of the tract’s location and shape, allowing fully automated tractography of several major WM tracts. In sum-mary, in the largest DWI analysis of ADHD to date, we inves-tigated the relationship of WM microstructure with lifetime and current ADHD diagnosis, dimensional scales of current symptoms, and ADHD-related impairment.
METHODS AND MATERIALS Participants
A full description of the study design is available in previous work (25). Subjects were part of the IMAGE (International Multicenter ADHD Genetics) study (wave 1 [W1]), which began in 2003 and included participants originally recruited with an ADHD diagnosis (probands), their affected and unaffected siblings, and healthy controls, as described previously (26). Data were collected at two centers: VU Amsterdam (Amster-dam, the Netherlands) and Radboudumc (Nijmegen, the Netherlands). After a mean follow-up period of 5.9 years (SD = 0.6 years), all W1 participants were invited for follow-up mea-surement. This second assessment wave (NeuroIMAGE1 [W2]) followed a phenotypic protocol similar to that of W1 but with the additional acquisition of MRI brain scans (25). A third assessment (NeuroIMAGE2 [W3]) occurred only in Nijmegen after approximately 3.7 years (SD = 0.6 years) and followed the same protocol as that of W2.
The sample for our current analyses (summarized inTable 1) includes all individuals from W2 who had DWI scans that passed all quality control (n = 570). We additionally included participants who had been newly recruited as part of W3 and thus had data available from only 1 wave (n = 84 after all quality control). There were thus 654 participants from 366 families in total in the current analysis (age range, 7.72–28.59 years; mean age = 17.41 years). Of these, 322 were unaffected, 258 had a current diagnosis of ADHD, and 74 had a diagnosis of sub-threshold ADHD at thefirst MRI acquisition time, as defined below. There were no differences on measures of ADHD severity (p = .941), age (p = .254), and sex (p = .165) between subjects in the current analysis and the whole W2 sample (n = 1085), including those who did not have available MRI data.
Clinical Assessments
A full description of the clinical assessments in our sample is available in previous work (25). ADHD diagnosis, clinical impairment, and symptom severity scores for IA and HI were determined through the Kiddie Schedule for Affective Disorder and Schizophrenia Present and Lifetime Version and the Conners’ Parent Rating Scale (CPRS) questionnaires at W1, W2, and W3 (27,28). An algorithm was applied to combine information from clinician interview and questionnaires, as detailed previously (25). A participant was diagnosed with ADHD according to DSM-IV criteria (if he/she had$6 IA and/or $6 HI symptoms for participants ,18 years of age; or $5 symptoms for participants $18 years of age), causing func-tional impairment at home, at school or work, or in social settings (29). Unaffected participants (including unaffected siblings and unrelated controls) were required to have a score of #3 in both symptom dimensions (#2 symptoms for
participants $18 years of age). Participants who fulfilled criteria for neither ADHD nor unaffected status were classified as subthreshold ADHD. The CPRS questionnaire was used to assess symptom severity because of its comprehensive symptom coverage for ADHD and psychometric properties more suitable for dimensional analyses. Multiple-setting clin-ical impairment was investigated separately, defined as impairment in at least 2 of the 3 domains according to the Kiddie Schedule for Affective Disorder and Schizophrenia Present and Lifetime Version interview: school/work, home, and social.
Imaging Acquisition and Analysis
MRI data were acquired with either a 1.5T MAGNETOM Sonata or a 1.5T AVANTO scanner (Siemens, Erlangen, Germany): twice-refocused pulsed gradient spin echo echo-planar im-aging; 60 diffusion-weighted directions; b-factor 1000 s/mm2;
Table 1. Demographic and Clinical Characteristics of the ADHD Affected, Subthreshold, and Unaffected Groups Based on Participants’ Diagnosis at the Time of Magnetic Resonance Imaging Acquisition
Affected Group (n = 258) Subthreshold Group (n = 74) Unaffected Group (n = 322) Test Statistic p Age, Years, Mean (SD) 17.4 (3.6) 18.2 (3.6) 17.2 (3.7) F1,658= 0.76 .38
IQ Estimate, Mean (SD) 95.1 (16.0) 102.2 (13.5) 104.3 (14.9) F2,653= 23.9 ,10210
Right-Handedness, n (%) 219 (85%) 61 (82%) 276 (86%) c2
4= 1.1 .89
Sex, Female, n (%) 76 (30%) 31 (42%) 170 (53%) c2
2= 34.6 ,1028
Scan Site Nijmegen, n (%) 148 (57%) 44 (59%) 167 (52%) c2
2= 5.1 .08
Head Motion, Mean (SD) 0.76 (1.1) 0.75 (1.3) 0.59 (0.4) F2,655= 3.1 .05
Diagnosed Ever, Yes, n (%) 258 (100%) 24 (32%) 20 (6.2%) CPRS HI1 IA, Mean (SD) 22.4 (12.1) 11.1 (9.6) 4.3 (4.9)
CPRS HI, Mean (SD) 9.6 (6.5) 3.7 (4.5) 1.3 (2.1)
CPRS IA, Mean (SD) 14.2 (7.1) 7.2 (5.6) 2.9 (3.5)
K-SADS-PL HI, Mean (SD) 5.2 (2.5) 2.8 (2.0) 2.1 (2.0) K-SADS-PL IA, Mean (SD) 7.0 (1.4) 4.7 (0.8) 6.0 (2.1)
Impaired, Yes, n (%) 252 (98%) 35 (47%) 12 (3.7%)
Duration Medication Use, Days, Mean (SD)a 1413 (332) 645 (1111) 46 (332) History of Medication Use, Yes, n (%)a 202 (78%) 27 (36%) 10 (3.1%)
Comorbidity, n (%)b
Conduct disorder 12 (4.7%) 1 (1.4%) 0 (0%) c2
2= 1.3 .53
Major depression 2 (0.8%) 0 (0%) 1 (0.3%) c2
2= 1.3 .52
Oppositional defiant disorder 76 (30%) 6 (8.1%) 5 (1.6%) c2
2= 93.5 ,10215
Substance Use Ever, n (%)c
Alcohol 52 (20%) 13 (18%) 51 (16%) c2 2= 1.7 .42 Tobacco 125 (48%) 27 (36%) 102 (32%) c2 2= 16.8 .0002 Cannabis or hash 72 (28%) 18 (24%) 57 (18%) c2 2= 8.9 .01 Other 26 (10%) 7 (9.5%) 15 (4.7%) c2 2= 6.9 .03
Demographic between-group differences were tested using F tests for continuous variables and c2tests for categorical variables. Totals are for
all participants who were included in the final sample, after all quality control. Medications were Ritalin (methylphenidate), Concerta (methylphenidate), Strattera (atomoxetine), and any other ADHD medication.
ADHD, attention-deficit/hyperactivity disorder; CPRS, Conners’ Parent Rating Scale; HI, hyperactivity-impulsivity; IA, inattention; K-SADS-PL, Kiddie Schedule for Affective Disorder and Schizophrenia Present and Lifetime Version.
aThe majority of patients were taking prescription medication for ADHD, mostly methylphenidate or atomoxetine. Duration of medication use was
recorded as the cumulative number of days of use, while the history of medication use was recorded as whether or not the participant had ever taken ADHD medication. Further details on medication use in our sample is available in previous work (60,61).
bHistory of comorbid disorders was screened for via the K-SADS-PL semi-structured interview (27,62). For children,12 years of age, the child’s
parents or researchers assisted in the completion of the self-report questionnaires. Participants with elevated scores on one or more of the K-SADS screening questions were further asked to complete a full supplement for each disorder. Thefinal diagnosis was based on DSM-IV criteria of each disorder.
cSubstance use at any point before assessment was recorded through self-report (63–65).
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5 non–diffusion-weighted images; interleaved slice acquisition; echo time/repetition time = 97 ms/8500 ms; GRAPPA (gener-alized autocalibrating partially parallel acquisition) acceleration 2; phase full Fourier; voxel size 2.0 3 2.0 3 2.2 mm. Both scanners were equipped with the same 8-channel phased-array head coil. More details of the T1-weighted and DWI data acquisition have been described previously (25). DWI images were preprocessed and included motion and eddy-current corrections (25,30). Diffusion tensor eigenvectors, eigenvalues, and FA were then calculated for each voxel (31). Bedpostx (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FDT/ UserGuide#BEDPOSTX) was applied with a 2-fiber model to estimate the distributions of the diffusion parameters for probabilistic tractography (32).
We then applied TRACULA (Tracts Constrained by Under-lying Anatomy) (http://surfer.nmr.mgh.harvard.edu/fswiki/ Tracula) (24), a FreeSurfer toolbox, to delineate 18 major WM tracts (Figure 1). TRACULA is a method for the automated reconstruction of major WM pathways based on an earlier well-known global probabilistic approach (33). TRACULA extends this algorithm by incorporating anatomical knowledge in the prior probability function so that thefinal segmented tract is the bestfit not only given the observed diffusion data within each subject, but also given its similarity to the known tract anatomy in relation to gray matter segmentations from FreeSurfer.
We used FreeSurfer 5.3 to define cortical and subcortical regions in the T1-weighted images of each individual (34). FreeSurfer segmentations were checked for quality based on a modified version of the ENIGMA Protocol 2.0 (http://enigma.ini. usc.edu/protocols/imaging-protocols/), resulting in 22 exclu-sions. Next, trac-all was run in TRACULA to segment all of the abovementioned tracts in native space. We visually checked the anatomical accuracy of each subject’s tract segmentation output, resulting in 7 exclusions (https://surfer.nmr.mgh. harvard.edu/fswiki/FreeviewGuide). Figure 1 illustrates TRA-CULA’s segmentation output in a single healthy control
subject. Finally, each participant’s scan was registered to Montreal Neurological Institute space for analyses of FA at each location along the tract.
Statistical Analyses
All statistical analyses were performed in R version 3.4.2 (R Foundation for Statistical Computing, Vienna, Austria). We investigated 4 types of effects and tested each separately as a fixed factor: 1) current ADHD diagnosis at the time of first MRI acquisition (state), 2) having ever been diagnosed with ADHD (trait), 3) clinical impairment, and 4) total symptom scores on HI and IA dimensions separately. In categorical analyses 1 and 2, the subthreshold group was excluded. In analysis 1, 322 par-ticipants were unaffected and 258 were affected with ADHD. For analysis 2, 312 participants had not been diagnosed with ADHD by the time of scan (never), while 292 had been diag-nosed by the time of scan (ever).
For all analyses, a stepwise approach was taken, in which first global effects across all tracts and interactions with tract were tested. Only if we found a significant overall effect or a significant interaction effect with tract did we examine the ef-fect of the independent variable in question on FA in individual tracts. For these tract-specific analyses, p values were Bon-ferroni corrected for the number of tracts, reported as family-wise error p ( pFWE) ( pFWE = p 3 18). For both global and by-tract analyses, we applied linear mixed-effects regression models (R package “lme4” version 1.1-21). All analyses incorporated age, sex, MRI acquisition site, assessment wave, and head motion (framewise displacement) asfixed effects and family membership as a random effect. Global models also included tract as afixed effect and subject as a random effect. SeeTable S7for a summary of our models.
If any tract was significant in the previous analyses, we performed a pointwise comparison of FA per voxel for every location along the tract.
anterior thalamic radia ons (ATR) cingulum-angular bundle (CAB) cingulum-cingulate gyrus bundle (CCG) cor cospinal tract (CST)
corpus callosum forceps major and minor (FMAJ, FMIN) inferior longitudinal fasciculus (ILF)
superior longitudinal fasciculus-parietal termina ons (SLFP) superior longitudinal fasciculus-temporal termina ons (SLFT) uncinate fasciculus (UNC)
Figure 1. Example pathway reconstruction and merged 4-dimensional volume output of TRACULA in a healthy control subject. All 18 white matter tracts (8 bilateral and 2 interhemispheric) are displayed at 20% of their maximum probability distribution threshold as an isosurface over the subject’s Free-Surfer segmentation, overlain on the subject’s diffusion data, and displayed from axial, coronal, and sagittal views.
In our post hoc analyses, we investigated effects of medica-tion use by separately adding use of any ADHD medicamedica-tion as a binary factor and then as a continuous factor. Potential effects of IQ, substance use, and major comorbidities were also separately analyzed through the addition of each variable as afixed factor. RESULTS
State and Trait Effects of ADHD Diagnosis
There was no association of ADHD state or ADHD trait with FA, globally across all tracts (Table 2). Both ADHD state and trait showed highly significant interaction effects with tract (p , .0006), which led us to further examine FA differences by tract (tract-specific models). However, there were no significant ef-fects on FA for any specific tract; in fact, all uncorrected p values were..05 (Tables S1andS2).
Supplementary analyses including subthreshold partici-pants showed nonsignificant effects of state or trait on global and tract-specific FA, though there were significant tract– interaction effects (p , .0005) (Table S8, S9, and S10). There were also no significant main or tract-by-group inter-action effects for each of the DSM-V presentation subtypes
separately (HI, IA, combined) on FA (Table S11). We also found no difference in global FA between unaffected siblings of affected individuals and unaffected controls with no affected sibling. Though there was a significant tract
Table 2. All Global Model Analyses
t df Standardizedb p Tract Interaction p State 20.924 489 2.011 .356 .000537a Trait 20.513 584 2.006 .608 .000003a Impairment 21.337 571 2.016 .182 .000020a CPRS HI1 IA 22.280 567 2.027 .023a .000140a CPRS HI 22.160 556 2.026 .031a .000017a CPRS IA 22.174 564 2.026 .030a .006162a
Associated t values, degrees of freedom, standardized beta coefficients, uncorrected p values, and interaction with tract p values for each overall fractional anisotropy analysis.
CPRS, Conners’ Parent Rating Scale; HI, hyperactivity-impulsivity; IA, inattention.
aSignificant global model for CPRS symptom dimension scores and
all tract interaction effects.
Figure 2. Tract-specific right cingulum angular bundle (rCAB) mean fractional anisotropy (FA) and symptom score. (Top panel) TRACULA (Tracts Con-strained by Underlying Anatomy) reconstruction in a healthy control of the rCAB in green and indicated by white arrows, from inferior (left), right (middle), and ventral anterior (right) views, respectively. The cingulum cingulate gyrus bundle is shown in blue for reference. (Bottom panel) Scatterplots with regression lines (95% confidence intervals) of the association between each dimension score (combined, hyperactivity-impulsivity [HI], and inattention [IA]) and the residuals (Resid.) of rCAB mean FA. Data points are grouped according to each participant’s diagnosis at wave 2 (W2). Mean FA in the rCAB had a negative association with HI symptom score but not with IA symptom score. SeeTable S4for associated statistics. CPRS, Conners’ Parent Rating Scale.
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interaction (p = .0005), there were no significant tract-specific models (Table S7).
Clinical Impairment
Our global model for the association between FA and impair-ment resulted in no association with impairimpair-ment and an inter-action effect with tract (p = .00002). No tracts had a significant association of FA with impairment (Table S3).
Continuous Symptom Scores
The global models indicated main effects of IA and HI CPRS scores (p = .030 and p = .031, respectively) and again signifi-cant interaction effects with tract (both p , .007) (Table 2). Tract-specific models for HI CPRS score showed a negative association between the right cingulum angular bundle (rCAB) FA and HI (t632=23.04, pFWE = .045). Figure 2 provides a closer inspection of this effect in the rCAB for each symptom dimension against FA. Other tracts did not show associations
between FA and either symptom dimension at Bonferroni-corrected significance levels (Figure 3andTable S4). Pointwise Comparison of FA Along rCAB
The effect of IA and HI scores on FA was relatively equally distributed along the length of the tract, with slightly larger negative effects at the posterior end compared with at the anterior end (Figure 4). There is no particular rCAB area or voxel that contributed significantly more than others to the association between FA and HI score, nor to that of FA and IA score (Table S5). Exploratory analyses of effects in all other tracts also revealed no significant differences (Figure S1). Post Hoc Analyses
Details and correlations between global and rCAB FA and medication use, comorbid disorders, substance use, and IQ are presented inTable 3. Days of medication use was nomi-nally associated with rCAB FA (p = .042). Therefore, to test for
Figure 3. All tract-specific models for the effect of Conners’ Parent Rating Scale symptom dimension (inattention [IA] and hyperactivity-impulsivity [HI]) on fractional anisotropy. Uncorrected p values and standardized bcoefficients: negative log of the p values are represented by size so that more signi fi-cant values are larger; more negativebcoefficients are displayed in darker color; the only significant tract-specific model is marked by an asterisk. See
Table S4 for associated statistics. ATR, anterior thalamic radiations; CAB, cingulum angular bundle; CCG, cingulum cingulate gyrus bundle; CST, corti-cospinal tract; FMAJ, corpus callosum forceps ma-jor; FMIN, corpus callosum forceps minor; ILF, inferior longitudinal fasciculus; SLFP, superior lon-gitudinal fasciculus parietal terminations; SLFT, su-perior longitudinal fasciculus temporal terminations; UNC, uncinate fasciculus.
0.2 0.1 0.0 -0.1 -0.2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 relative location along rCAB (posterior to anterior)
β
Figure 4. Pointwise comparison of fractional anisotropy (FA) along the right cingulum angular bundle (rCAB). Colored lines show the standardized bcoefficients (95% confidence intervals) of the as-sociations between FA along the length of the rCAB and the symptom score, grouped by symptom dimension, shown from posterior to anterior ends of the tract.bcoefficients of FA associations with the hyperactivity-impulsivity symptom score are in red, and those with inattention are in blue for each of the 27 cross-sections along the standard-space tract spline. Toward each endpoint of the tract, fewer participants contributed FA data to the analysis, reflected in wider confidence intervals at the tract ends. SeeTable S5for associated statistics.
potential confounding effects of medication use duration, we added it as a covariate to the previously significant model of HI on FA in the rCAB. When medication use duration was included as a covariate, the effect of HI was no longer signif-icant at our Bonferroni-corrected significance level (t570=22.207, p = .028), but neither was the effect of medi-cation itself (t570=21.223, p = .222). Medication use and HI symptom scores were highly collinear, given a correlation when cases and controls were pooled (Spearman’s r = .661). Medication use did not have an interaction effect with HI scores (p . .069).
Head motion, age, and sex were already included as covariates in all models. There were no significant main or interaction effects of any of these variables with any inde-pendent variable of interest on FA for all analyses reported above. Although affected and subthreshold individuals dis-played greater head motion than those who were unaffected, this difference was not significant (Table 1), and there was never an interaction effect with motion in all analyses.
DISCUSSION
We performed a large, systematic analysis of ADHD clinical measures with WM microstructure using automated tractog-raphy. The presence of significant interactions with tract in-dicates that for all ADHD-related variables tested, ADHD’s effect on FA varies across the different WM tracts. However, we did notfind any evidence for FA reductions or increases in association with ADHD diagnosis, neither as a trait measure nor as a state measure, nor with impairment, across 18 major WM tracts. In contrast, regarding the HI symptom domain as a dimensional measure, our results showed reduced FA in the temporal portion of the cingulum bundle, or rCAB. Hence, our findings here suggest that continuous measures of current
symptom severity state may be more sensitive to differences in FA in association with ADHD than are categorical clinical measures.
The cingulum has been previously implicated in voxel-based studies of ADHD, but with mixed exact locations and directions of effects (2,35,36). This may be due to differences in DWI analyses, wherein the cingulum is not easily delineated by various analytic methods. Only some connections encom-pass the entire length of the tract, and discrete subdivisions of the cingulum display distinct FA measures at different medial-lateral positions within the bundle, indicating qualitative changes along the length of the tract (35–37). The present separation of the CAB from its dorsal counterpart may have provided greater resolution for identifying associations that were heretofore difficult to detect or were manifested differ-ently. Moreover, our rCAB pointwise analysis revealed that IA and HI symptom effects on FA are comparable for all voxels, which indicates that our result was driven by a relatively subtle effect across the entire tract—an effect that could be more challenging to detect with voxel-based or less sophisticated tractography methods.
The CAB links the posterior default mode network (DMN) and the medial temporal lobe. Aberrant functional connectivity involving the DMN is one of the most consistent neuroimaging characteristics of ADHD (38–42). Greater severity of HI and IA have also been associated with decreased DMN connectivity (43,44). The DMN is characterized by increased activation during rest and mind wandering and has also been associated with emotional lability (45–51). Mind wandering has been associated with emotional lability and greater ADHD symptom severity (52). Ourfinding of reduced FA in the rCAB could speculatively be an anatomical substrate of the frequently observed altered DMN functional connectivity in ADHD, and the associated emotional problems and increased mind
Table 3. Post Hoc Analyses
Variable
Global FA Tract-Specific rCAB FA
Standardizedb t p Standardizedb t p
Medication Use
Binary (yes or no) 2.003 20.206 .837 2.033 20.845 .399
Continuous, days 2.010 20.803 .422 2.078 22.039 .042a Comorbidities (Yes or No) Conduct disorder 2.037 20.714 .480 2.004 20.116 .908 Major depression .095 1.975 .051 2.018 20.512 .609 Oppositional defiant disorder 2.016 21.352 .177 2.059 21.626 .105 Substance Use (Yes or No) Alcohol 2.003 20.211 .833 2.005 20.142 .887 Tobacco .009 0.660 .510 .010 0.231 .818 Cannabis or hash .002 0.118 .906 .052 1.252 .211 Other drugs 2.010 20.719 .473 2.012 20.313 .754 IQ Estimate 2.010 20.837 .403 .012 0.315 .753
The standardizedbcoefficients, associated t values, and uncorrected p values for the regression of medication use, comorbidities, and drug use until the date of magnetic resonance imaging scan against global and rCAB FA. We found a negative effect of total duration of medication use as a continuous variable on FA but not history of medication use as a binary factor. No other factors had a significant effect on either global or rCAB FA.
FA, fractional anisotropy; rCAB, right cingulum angular bundle.
aSignificant effect on FA in rCAB.
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wandering that may stem from aberrant activation in the DMN and its connections with the medial temporal lobe.
Given the current largest DWI ADHD sample yet and our use of a validated robust global probabilistic tractography method, the persistence of null results in association with ADHD diag-nosis is noteworthy. Unlike most case-control studies, we had an advantage in that we were able to test current diagnostic status, as well as previous (usually unknown) history of diag-nosis. Yet, we found no indication of an effect for either a trait or state on WM microstructure. This does not necessarily mean that WM plays no role in ADHD remission or persistence over time; future longitudinal studies can better address this as they can account for interindividual differences in clinical severity and brain features at baseline. Similar to diagnosis, we did notfind an association between FA and impairment, which corroborates our earlier supposition that symptom severity neither directly nor strongly maps onto impairment. Explana-tions may be that impairment is partly context dependent (e.g., a supportive structured environment may lead to less hindrance from ADHD symptoms, as opposed to a chaotic unsafe environment), related to compensatory skills (e.g., intelligence or executive skills), and/or to possible comorbid symptoms.
We can further speculate as to why our analyses did not generally replicate the rather large literature reporting WM al-terations related to ADHD. Our participants were adolescents; thus, our negativefinding may imply WM case-control differ-ences attenuate with age, similar to gray matter (53). However, a systematic review of DWI studies does not indicate that WM alterations are found more in younger populations (3). Perhaps the 1.5Tfield strength was not enough to detect our effects of interest, as opposed to 3T scanners that are more widely used today. Nonetheless, a systematic review of DWI studies did not indicate that 3T scanners are more likely to produce significant case-control differences in ADHD (3). Head motion can pro-duce spurious DWI findings, which is of particular concern regarding ADHD, given that HI is associated with greater motion (54,55). A previous TRACULA analysis of group dif-ferences in DWI measures demonstrated that several tracts, including the rCAB, had greater group differences in DWI measures when group differences in head motion were higher (55). Until recently, many DWI studies of ADHD did not examine group differences in head motion, and similar to our categorical results here, most studies that reported no case-control difference in motion also had null results (3). All of our analyses included head motion as a covariate, and each model was checked for interaction effects with head motion.
Days of medication use was negatively associated with rCAB FA and, when added into our rCAB model, resulted in a loss of the main effect of HI score on FA. This limitation is not surprising, given that—especially when cases and controls were pooled—ADHD medication use and symptom score were highly correlated. High levels of collinearity between medica-tion and symptom severity means that, within the context of this study design, we cannot clearly separate these effects. In contrast to our observations here, other studies have shown that methylphenidate treatment is associated with higher global FA, while other more suitably designed DTI studies that specifically investigated medication effects in ADHD
patients did notfind confounding effects of medication use on FA (53,56,57). The attenuated significance of both medication and HI symptoms when included in the same model tentatively supports an effect of ADHD symptom-atology in combination with a secondary effect of medica-tion use—merely owing to higher medication use by those with more symptoms.
Finally, we must note that the physiological interpretation of DWIfindings remains somewhat speculative (15,58). For example, in a singlefiber bundle, decreased FA may repre-sent myelin breakdown or reduced axonal integrity, whereas in regions with crossing fibers, it may represent increased neuronal branching and could, as such, even indicate increased structural connectivity. Hence, we must be cautious in interpreting our findings in terms of specific neurobiological mechanisms. In contrast to many previous studies that used tract-based spatial statistics or other voxel-based methods, we used tractography. This allows locating any FA differences tofiber tracts with known anatomy, which aids in relating it to brain networks and brain function. In addition, on the one hand, tractography allows for more intersubject variation in the shape and size offibers and the total brain, without affecting the FA values within the voxels. On the other hand, tractography is less sensitive to the very subtle but spatially consistent effects that are often reported using tract-based spatial statistics in combination with threshold-free cluster enhancement (59). Thus, differences in spatial location and extent between our study and previous DWI studies in ADHD should be interpreted bearing in mind these methodological choices.
Conclusions
In conclusion, to our knowledge, this is thefirst time that global probabilistic tractography has been applied to an ADHD DWI dataset of this size. Using TRACULA allowed for the automatic, simultaneous extraction of FA measures from hundreds of subjects. In line with previous data, we provide further evi-dence that accentuates the complexity of this disorder and complements information from case-control studies. We found that FA is associated with symptom dimension scores but not with categorical diagnostic ADHD measures. We conclude that associations of FA with ADHD may be more subtle than was previously thought, and that the rCAB may be more involved in the symptoms of ADHD than was previously thought.
ACKNOWLEDGMENTS AND DISCLOSURES
The NeuroIMAGE study was supported by National Institutes of Health Grant No. R01MH62873 (to Stephen V. Faraone), Dutch Research Council (NOW) Large Investment Grant No. 1750102007010 (to JKB), NWO Brain & Cognition Grant Nos. 056-13-015 (to JKB) and 433-09-242 (to JKB), ZonMW Grant No. 60-60600-97-193 (to JKB), and matching grants from Radboud University Medical Center (to JKB), University Medical Center Groningen and Accare (to PJH and CAH), and Vrije Universiteit Amsterdam (to JO). JN is supported by the NWO through a personal Veni grant from the Innovation Program (Grant No. VI.Veni.194.032). BF is supported by the NWO through a personal Vici grant (Grant No. 016-130-669) and a pilot grant from the Dutch National Research Agenda for the NeuroLabNL project (Grant No. 400 17 602). ES is supported by a Hypatia Tenure Track Grant (Radboudumc), Christine Mohrmann Fellowship (Radboud University), and a NARSAD Young Investigator Grant (Brain and Behavior Research Foundation) Grant No. 25034).
This article was published as a preprint on bioRxiv:https://www.biorxiv. org/content/10.1101/787713v3.
BF has received educational speaking fees from Medic. JKB has served as a consultant to/member of advisory board of and/or speaker for Janssen Cilag BV, Eli Lilly, Bristol-Myer Squibb, Shering Plough, UCB, Shire, Novartis, and Servier. He is not an employee of any of these companies, nor a stock shareholder of any of these companies. He has no otherfinancial or material support, including expert testimony, patents, or royalties. All other authors report no biomedical financial interests or potential conflicts of interest.
ARTICLE INFORMATION
From the Department of Cognitive Neuroscience (CGD, RJMC, MPZ, DvR, SEAA, JN, JKB, CFB, ES), Centre for Cognitive Neuroimaging (CGD, RJMC, MPZ, DvR, SEAA, JN, JKB, CFB, ES), Department of Human Genetics (BF), and Department of Psychiatry (BF), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center; and Karakter Child and Adolescent Psychiatry University Centre (JKB), Nijmegen; Department of Psychiatry (PJH, CAH), University Medical Center Groningen, University of Groningen; and Department of Clinical Neuropsychology (JO), Vrije Uni-versiteit Amsterdam, Amsterdam, the Netherlands; and the Centre for Functional MRI of the Brain (CFB), University of Oxford, Oxford, United Kingdom.
Address correspondence to Christienne G. Damatac, M.Sc., at
c.gonzalesdamatac@donders.ru.nl.
Received Mar 27, 2020; revised and accepted Jul 21, 2020.
Supplementary material cited in this article is available online athttps:// doi.org/10.1016/j.bpsc.2020.07.015.
REFERENCES
1. Faraone SV, Biederman J, Mick E (2006): The age-dependent decline of attention deficit hyperactivity disorder: A meta-analysis of follow-up studies. Psychol Med 36:159–165.
2. Faraone SV, Asherson P, Banaschewski T, Biederman J, Buitelaar JK, Ramos-Quiroga JA, et al. (2015): Attention-deficit/hyperactivity disor-der. Nat Rev Dis Primers 1:15020.
3. Aoki Y, Cortese S, Castellanos FX (2018): Research Review: Diffusion tensor imaging studies of attention-deficit/hyperactivity disorder: Meta-analyses and reflections on head motion. J Child Psychol Psy-chiatry Allied Discip 59:193–202.
4. Sudre G, Mangalmurti A, Shaw P (2018): Growing out of attention deficit hyperactivity disorder: Insights from the ‘remitted’ brain. Neu-rosci Biobehav Rev 94:198–209.
5. Francx W, Oldehinkel M, Oosterlaan J, Heslenfeld D, Hartman CA, Hoekstra PJ, et al. (2015): The executive control network and symp-tomatic improvement in attention-deficit / hyperactivity disorder. Cortex 73:62–72.
6. Francx W, Zwiers MP, Mennes M, Oosterlaan J, Heslenfeld D, Hoekstra PJ, et al. (2015): White matter microstructure and develop-mental improvement of hyperactive/impulsive symptoms in attention-deficit/hyperactivity disorder. J Child Psychol Psychiatry Allied Discip 56:1289–1297.
7. Francx W, Llera A, Mennes M, Zwiers MP, Faraone SV, Oosterlaan J, et al. (2016): Integrated analysis of gray and white matter alterations in attention-deficit/hyperactivity disorder. Neu-roImage Clin 11:357–367.
8. van Ewijk H, Heslenfeld DJ, Zwiers MP, Buitelaar JK, Oosterlaan J (2012): Diffusion tensor imaging in attention deficit/hyperactivity dis-order: A systematic review and meta-analysis. Neurosci Biobehav Rev 36:1093–1106.
9. van Ewijk H, Heslenfeld DJ, Zwiers MP, Faraone SV, Luman M, Hartman CA, et al. (2014): Different mechanisms of white matter ab-normalities in attention-deficit/hyperactivity disorder: A diffusion tensor imaging study. J Am Acad Child Adolesc Psychiatry 53:790– 799.e3.
10. Shaw P, Sudre G, Wharton A, Weingart D, Sharp W, Sarlls J (2015): White matter microstructure and the variable adult outcome of
childhood attention deficit hyperactivity disorder. Neuro-psychopharmacology 40:746–754.
11. Onnink AMH, Zwiers MP, Hoogman M, Mostert JC, Dammers J, Kan CC, et al. (2015): Deviant white matter structure in adults with attention-deficit/hyperactivity disorder points to aberrant myelination and affects neuropsychological performance. Prog Neuro-psychopharmacol Biol Psychiatry 63:14–22.
12. Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H, Lori N, et al. (2015): Histological validation of high-resolution DTI in human post mortem tissue. Front Neuroanat 9:98.
13. Mollink J, Kleinnijenhuis M, Cappellen van Walsum AM van, Sotiropoulos SN, Cottaar M, Mirfin C, et al. (2017): Evaluating fibre orientation dispersion in white matter: Comparison of diffusion MRI, histology and polarized light imaging. NeuroImage 157:561–574. 14. Chang EH, Argyelan M, Aggarwal M, Chandon TS, Karlsgodt KH,
Mori S, Malhotra AK (2017): The role of myelination in measures of white matter integrity: Combination of diffusion tensor imaging and two-photon microscopy of CLARITY intact brains. NeuroImage 147:253–261.
15. Beaulieu C (2002): The basis of anisotropic water diffusion in the nervous system - A technical review. NMR Biomed 15:435–455. 16. Winston GP (2012): The physical and biological basis of quantitative
parameters derived from diffusion MRI. Quant Imaging Med Surg 2:254–265.
17. Cortese S, Imperati D, Zhou J, Proal E, Klein RG, Mannuzza S, et al. (2013): White matter alterations at 33-year follow-up in adults with childhood attention-deficit/hyperactivity disorder. Biol Psychiatry 74:591–598.
18. Coghill D, Sonuga-Barke EJS (2012): Annual research review: Cate-gories versus dimensions in the classification and conceptualisation of child and adolescent mental disorders - Implications of recent empirical study. J Child Psychol Psychiatry Allied Discip 53:469–489.
19. Marcus DK, Barry TD (2011): Does attention-deficit/hyperactivity
dis-order have a dimensional latent structure? A taxometric analysis. J Abnorm Psychol 120:427–442.
20. Haslam N, Williams B, Prior M, Haslam R, Graetz B, Sawyer M (2006): The latent structure of attention-deficit/hyperactivity disorder: A taxometric analysis. Aust N Z J Psychiatry 40:639–647.
21. Lahey BB, Willcutt EG (2010): Predictive validity of a continuous alternative to nominal subtypes of attention-deficit/hyperactivity dis-order for DSM-V. J Clin Child Adolesc Psychol 39:761–775. 22. Wu ZM, Bralten J, Cao QJ, Hoogman M, Zwiers MP, An L, et al. (2017):
White matter microstructural alterations in children with ADHD: Cate-gorical and dimensional perspectives. Neuropsychopharmacology 42:572–580.
23. Skirrow C, Asherson P (2013): Emotional lability, comorbidity and impairment in adults with attention-deficit hyperactivity disorder. J Affect Disord 147:80–86.
24. Yendiki A, Panneck P, Srinivasan P, Stevens A, Zöllei L, Augustinack J, et al. (2011): Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front Neuroinform 5:23.
25. von Rhein D, Mennes M, van Ewijk H, Groenman AP, Zwiers MP, Oosterlaan J, et al. (2015): The NeuroIMAGE study: A prospective phenotypic, cognitive, genetic and MRI study in children with attention-deficit/hyperactivity disorder. Design and descriptives. Eur Child Adolesc Psychiatry 24:265–281.
26. Müller UC, Asherson P, Banaschewski T, Buitelaar JK, Ebstein RP, Eisenberg J, et al. (2011): The impact of study design and diagnostic approach in a large multi-centre ADHD study. Part 1: ADHD symptom patterns. BMC Psychiatry 11:54.
27. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, et al. (1997): Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): Initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 36:980–988.
28. Conners CK, Erhardt D, Epstein JN, Parker JDA, Sitarenios G, Sparrow E (1999): Self-ratings of ADHD symptoms in adults I: Factor structure and normative data. J Atten Disord 3:141–151.
White Matter in ADHD: Tractography in 654 Individuals
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging-2020;-:-–-www.sobp.org/BPCNNI 9
Biological Psychiatry: CNNI
29. American Psychiatric Association (2012): The Diagnostic and Statis-tical Manual of Mental Disorders, 4th ed. Washington, DC: American Psychiatric Press.
30. Zwiers MP (2010): Patching cardiac and head motion artefacts in diffusion-weighted images. NeuroImage 53:565–575.
31. Behrens TEJ, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, et al. (2003): Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 50:1077–1088. 32. Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW
(2007): Probabilistic diffusion tractography with multiplefibre orienta-tions: What can we gain? NeuroImage 34:144–155.
33. Jbabdi S, Woolrich MWW, Andersson JLRLRR, Behrens TEJEJJ (2007): A Bayesian framework for global tractography. NeuroImage 37:116–129.
34. Fischl B (2012): FreeSurfer. NeuroImage 62:774–781.
35. Heilbronner SR, Haber SN (2014): Frontal cortical and subcortical projections provide a basis for segmenting the cingulum bundle: Im-plications for neuroimaging and psychiatric disorders. J Neurosci 34:10041–10054.
36. Jones DK, Christiansen KF, Chapman RJ, Aggleton JP (2013): Distinct subdivisions of the cingulum bundle revealed by diffusion MRIfibre tracking: Implications for neuropsychological investigations. Neuro-psychologia 51:67–78.
37. Bubb EJ, Metzler-Baddeley C, Aggleton JP (2018): The cingulum bundle: Anatomy, function, and dysfunction. Neurosci Biobehav Rev 92:104–127.
38. Konrad K, Eickhoff SB (2010): Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum Brain Mapp 31:904–916.
39. Posner J, Park C, Wang Z (2014): Connecting the dots: A review of resting connectivity MRI studies in attention-deficit/hyperactivity dis-order. Neuropsychol Rev 24:3–15.
40. Marcos-Vidal L, Martínez-García M, Pretus C, Garcia-Garcia D, Martínez K, Janssen J, et al. (2018): Local functional connectivity suggests functional immaturity in children with attention-deficit/ hyperactivity disorder. Hum Brain Mapp 39:2442–2454.
41. van Rooij D, Hoekstra PJ, Mennes M, von Rhein D, Thissen AJAM, Heslenfeld D, et al. (2015): Distinguishing adolescents with ADHD from their unaffected siblings and healthy comparison subjects by neural activation patterns during response inhibition. Am J Psychiatry 172:674–683.
42. Sidlauskaite J, Sonuga-Barke E, Roeyers H, Wiersema JR (2016): Altered intrinsic organisation of brain networks implicated in atten-tional processes in adult attention-deficit/hyperactivity disorder: A resting-state study of attention, default mode and salience network connectivity. Eur Arch Psychiatry Clin Neurosci 266:349–357. 43. Elton A, Alcauter S, Gao W (2014): Network connectivity abnormality
profile supports a categorical-dimensional hybrid model of ADHD. Hum Brain Mapp 35:4531–4543.
44. von Rhein D, Beckmann CF, Franke B, Oosterlaan J, Heslenfeld DJ, Hoekstra PJ, et al. (2017): Network-level assessment of reward-related activation in patients with ADHD and healthy individuals. Hum Brain Mapp 38:2359–2369.
45. Godwin CA, Hunter MA, Bezdek MA, Lieberman G, Elkin-Frankston S, Romero VL, et al. (2017): Functional connectivity within and between intrinsic brain networks correlates with trait mind wandering. Neuro-psychologia 103:140–153.
46. Christoff K, Gordon AM, Smallwood J, Smith R, Schooler JW (2009): Experience sampling during fMRI reveals default network and execu-tive system contributions to mind wandering. Proc Natl Acad Sci U S A 106:8719–8724.
47. Mittner M, Hawkins GE, Boekel W, Forstmann BU (2016): A neural model of mind wandering. Trends Cogn Sci 20:570–578.
48. Greicius MD, Krasnow B, Reiss AL, Menon V (2003): Functional con-nectivity in the resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 100:253–258.
49. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001): A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682.
50. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005): The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102:9673–9678.
51. Martins B, Mather M (2016): Default mode network and later-life emotion regulation: Linking functional connectivity patterns and emotional outcomes. In: Ong AD, Löckenhoff CE, editors. Bronfen-brenner Series on the Ecology of Human Development. Emotion, Aging, and Health. Washington, DC: American Psychological Associ-ation, 9–29.
52. Helfer B, Cooper RE, Bozhilova N, Maltezos S, Kuntsi J, Asherson P (2019): The effects of emotional lability, mind wandering and sleep quality on ADHD symptom severity in adults with ADHD. Eur Psychi-atry 55:45–51.
53. Hoogman M, Bralten J, Hibar DP, Mennes M, Zwiers MP, Schweren LSJ, et al. (2017): Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: A cross-sectional mega-analysis. Lancet Psychiatry 4:310–319.
54. García Murillo L, Cortese S, Anderson D, Di Martino A, Castellanos FX (2015): Locomotor activity measures in the diagnosis of attention deficit hyperactivity disorder: Meta-analyses and new findings. J Neurosci Methods 252:14–26.
55. Yendiki A, Koldewyn K, Kakunoori S, Kanwisher N, Fischl B (2014): Spurious group differences due to head motion in a diffusion MRI study. NeuroImage 88:79–90.
56. Bouziane C, Filatova OG, Schrantee A, Caan MWA, Vos FM, Reneman L (2019): White matter by diffusion MRI following methylphenidate treatment: A randomized control trial in males with attention-deficit/hyperactivity disorder. Radiology 293:186–192.
57. Bouziane C, Caan MWA, Tamminga HGH, Schrantee A (2018): ADHD and maturation of brain white matter: A DTI study in medication naive children and adults. NeuroImage Clin 17:53–59.
58. Jones DK, Knösche TR, Turner R (2013): White matter integrity,fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. NeuroImage 73:239–254.
59. Sprooten E, Barrett J, McKay DR, Knowles EE, Mathias SR, Winkler AM, et al. (2016): A comprehensive tractography study of patients with bipolar disorder and their unaffected siblings. Hum Brain Mapp 37:3474–3485.
60. Groenman AP, Schweren LJS, Weeda W, Luman M, Noordermeer SDS, Heslenfeld DJ, et al. (2019): Stimulant treatment profiles predicting co-occurring substance use disorders in individuals with attention-deficit/hyperactivity disorder. Eur Child Adolesc Psy-chiatry 28:1213–1222.
61. Schweren LJS, Groenman A, Von Rhein D, Weeda W, Faraone SF, Luman M, et al. (2017): Stimulant treatment trajectories are associated with neural reward processing in attention-deficit/hyperactivity disor-der. J Clin Psychiatry 78:e790–e796.
62. Donker T, Comijs H, Cuijpers P, Terluin B, Nolen W, Zitman F, Penninx B (2010): The validity of the Dutch K10 and extended K10 screening scales for depressive and anxiety disorders. Psychiatry Res 176:45–50.
63. Gavin DR, Ross HE, Skinner HA (1989): Diagnostic validity of the drug abuse screening test in the assessment of DSM-III drug disorders. Br J Addict 84:301–307.
64. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO (1991): The Fagerström Test for Nicotine Dependence: A revision of the Fager-ström Tolerance Questionnaire. Br J Addict 86:1119–1127. 65. Saunders JB, Aasland OG, Babor TF, de la Fuente JR, Grant M (1993):
Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption–II. Addiction 88:791–804.
